Object Goal Navigation using Data Regularized Q-Learning

TL;DR

This paper presents a data-regularized deep reinforcement learning approach for object goal navigation, enabling robots to efficiently find objects in unseen environments by building semantic maps and selecting long-term goals.

Contribution

It introduces a novel framework combining semantic mapping with data augmentation and Q-function regularization for improved goal selection in object navigation.

Findings

Significant performance improvements over baseline methods.

Effective long-term goal selection using semantic maps.

Robustness demonstrated in photo-realistic simulation environment.

Abstract

Object Goal Navigation requires a robot to find and navigate to an instance of a target object class in a previously unseen environment. Our framework incrementally builds a semantic map of the environment over time, and then repeatedly selects a long-term goal ('where to go') based on the semantic map to locate the target object instance. Long-term goal selection is formulated as a vision-based deep reinforcement learning problem. Specifically, an Encoder Network is trained to extract high-level features from a semantic map and select a long-term goal. In addition, we incorporate data augmentation and Q-function regularization to make the long-term goal selection more effective. We report experimental results using the photo-realistic Gibson benchmark dataset in the AI Habitat 3D simulation environment to demonstrate substantial performance improvement on standard measures in comparison with a state of the art data-driven baseline.